IT and Productivity in U.S. Manufacturing: Do Computer Networks Matter?

Article excerpt

I. INTRODUCTION

How computers affect productivity is a long-standing research question. Many recent studies argue that information technology (IT), particularly computers, is a significant source of U.S. productivity. The specific mechanism remains elusive. Detailed data on the use of computers and computer networks have been scarce. This article uses new plant-level data on computer networks collected by the U.S. Census Bureau to estimate the effect of computer networks on labor productivity across U.S. manufacturing plants. The Computer Network Use Supplement (CNUS) to the 1999 Annual Survey of Manufactures (ASM) focused on the use of computer networks, rather than the presence of computers alone. Linking the CNUS data to current and previous information for the same plants collected in the 1999 ASM, and the 1997 and 1992 Census of Manufactures (CM), allows us to examine the relationship between productivity and the use of computer networks.

Our work differs from others in several important aspects. First, this is the first study linking the use of computer networks to labor productivity, using data for approximately 30,000 plants representing the U.S. manufacturing sector. Most previous studies examining the link between productivity and computers or other IT in the United States focus on the presence of computers, using either data on the stock of computer capital or on current IT or computer investment as proxies for the computer stock. Only one previous study, McGuckin et al. (1998), examined the link between productivity and how computers were used. That study was limited to five U.S. two-digit manufacturing industries covered in the 1988 and 1993 Surveys of Manufacturing Technology (SMT) collected by the U.S. Census Bureau and did not separate the use of computer networks from other uses of computers and advanced technologies.

Second, we extend the existing model relating IT to productivity by including materials as a separate input. Our dependent variable is a gross-output measure of labor productivity. Although gross output is an appropriate measure of the theoretical output, particularly at the plant level, most previous plant-level studies for the United States use a value-added measure and exclude materials as a factor input, possibly making their results subject to omitted variable biases.

Third, we model the probability that a plant has a computer network as a function of its performance and conditions in prior periods. This probability is of interest in its own right. It also makes possible the fourth distinguishing feature of our work, testing for possible endogeneity problems associated with the computer network variable. If good plants are more likely to have computer networks, we must account for these characteristics to get a more accurate estimate of the effect of networks.

Our research has five principal findings. First, average labor productivity is higher in manufacturing plants with networks than in plants without networks. Second, computer networks have a positive and significant effect on labor productivity after controlling for other important factors, such as capital intensity and other plant characteristics. Third, the choice of theoretical model has empirical consequences. Previous studies using value-added models appear to overstate the effect of IT on productivity by factors of two to three. Fourth, plants with lower relative productivity in previous periods are more likely to have computer networks. Fifth, computer networks have a positive and significant effect on labor productivity even after taking account of possible endogeneity of the computer network variable.

II. BACKGROUND

Computers play an important role in the strong economic performance of the U.S. economy according to many recent studies. This role is particularly important during the surge of productivity growth in the late 1990s as discussed in Oliner and Sichel (2000), Jorgenson and Stiroh (2000), Jorgenson (2001), Stiroh (2001), and Triplett and Bosworth (2000). …